Senior Machine Learning Engineer

Summary

Join Reddit's LS Embedding team as a Senior Machine Learning Engineer! Design, develop, and optimize graph-based ML models for large-scale recommendation systems. You will work on embedding generation, distributed training, and scalable serving architectures. Collaborate with cross-functional teams and contribute to cutting-edge ML research applied at scale. This role requires 5+ years of experience in machine learning engineering with a focus on recommendation systems and deep learning. Reddit offers a competitive salary and comprehensive benefits.

Requirements

• 5+ years of experience in machine learning engineering, with a strong focus on recommendation systems, representation learning, and deep learning
• Hands-on experience with Graph Neural Networks (GNNs), collaborative filtering, and large-scale embeddings
• Proficiency in Python and experience with ML frameworks such as PyTorch Geometric (PyG), Deep Graph Library (DGL), TensorFlow, or JAX
• Strong understanding of graph theory, network science, and representation learning techniques
• Experience building distributed training and inference systems using ML infrastructure components (data parallelism, model pruning, inference optimization, etc.)
• Ability to work in a fast-paced environment, balancing innovation with high-quality production deployment
• Strong communication skills and the ability to collaborate cross-functionally with engineers, researchers, and product teams

Responsibilities

• Design and implement scalable, high-performance machine learning models using Graph Neural Networks (GNNs), transformers, and knowledge graph approaches
• Develop and optimize large-scale embedding generation pipelines for Reddit’s recommendation systems
• Collaborate with ML infrastructure teams to enable efficient distributed training (multi-GPU, model/data parallelism) and low-latency serving
• Work closely with cross-functional teams (Ads, Feed Ranking, Content Understanding) to integrate embeddings into various personalization and ranking systems
• Drive feature engineering efforts, identifying and curating expressive raw data to enhance model effectiveness
• Monitor, evaluate, and improve model performance using A/B testing, offline metrics, and real-time feedback loops
• Stay up-to-date with the latest research in GNNs, transformers, and representation learning, bringing new ideas into production
• Participate in code reviews, mentor junior engineers, and contribute to technical decision-making

Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k Match
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Reddit Global Days off
• Generous paid Parental Leave
• Paid Volunteer time off
• Medical, dental, and vision insurance
• 401(k) program with employer match
• Generous time off for vacation
• Parental leave